Dental Public Health in Action: Understanding oral health care needs and oral health-related quality of life in vulnerable adults in Plymouth.

This paper aims to provide a snapshot analysis of the oral health status of vulnerable adults in Plymouth; and to demonstrate the extent to which oral disease impacts on their normal functioning through the implementation of the Oral Health Impact Profile (OHIP). It is acknowledged that gaining a representative sample of a transient population such as people who are homeless, or individuals affected by problematic use of drugs and/or alcohol is difficult. An opportunity was identified to collect data within the Community Engagement Team's (CET) programme of activity within the Peninsula Dental Social Enterprise. The CET works alongside local organisations to enable dental students from Peninsula School of Dentistry to undertake outreach programmes in a variety of settings. A study was designed which aimed to analyse the oral health status of vulnerable adults accessing three day-support services in Plymouth, and to understand the extent to which oral disease impacts on their normal functioning through the OHIP-14. For all impact domains, the 44 patients in this study reported a greater impact than that found in the Adult Dental Health Survey. The most commonly reported impact domains were physical pain and psychological discomfort. The sample was divided into high and low oral health-related quality of life impact groups, and those participants in the high impact group had significantly greater median D3MFT scores, i.e. higher levels of decay experience. This survey highlights how these vulnerable groups are characterised by a high prevalence of poor oral health, ill-health, deprivation and social exclusion.

Obesity and Dental Caries in Young Children in Plymouth, United Kingdom: A Spatial Analysis.

OBJECTIVE: To examine the spatial clustering of obesity and dental caries in young children in Plymouth, United Kingdom, to evaluate the association between these conditions and deprivation, and explore the impact of neighbourhood-level characteristics on their distribution. BASIC RESEARCH DESIGN: Cross-sectional study analysing data from the National Child Measurement Programme (N=2427) and the Local Dental Health Survey (N=1425). The association of deprivation with weight status and caries was determined at individual and area level, using ANOVA and Poisson models. The overall spatial clustering was assessed using a modified version of the Global Moran's I, while clusters were located through Local Indicators of Spatial Association. Spatial autocorrelation was assessed using the variograms of the raw values. Log-linear Poisson models were fitted to assess the significance of neighbourhood characteristics on overweight/obesity and caries distribution. RESULTS: At an individual level, deprivation was not associated with BMI z-scores but was a significant predictor of caries (p⟨0.05). However, at area level, deprivation related to the rates of both conditions. A significant positive autocorrelation was observed across neighbourhoods for caries. The variograms suggested spatial autocorrelations up to 2.5 km and 3 km for overweight/obesity and caries, respectively. Among several neighbourhood characteristics, the proportion of people on benefits was found to be a significant predictor of caries rates. CONCLUSIONS: Our results underline the importance of considering geographic location and characteristics of the broader environment when developing strategies to target obesity and caries.

A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections.

The AEGISS (Ascertainment and Enhancement of Disease Surveillance and Statistics) project uses spatio-temporal statistical methods to identify anomalies in the incidence of gastrointestinal infections in the UK. The focus of this paper is the modelling of temporal variation in incidence using data from the Southampton area in southern England. We identified and fitted a hierarchical stochastic model for the time series of daily incident cases to enable probabilistic prediction of temporal variation in risk, and demonstrated the resulting gains in predictive accuracy by comparison with a conventional analysis based on an over-dispersed Poisson log-linear regression model. We used Bayesian methods of inference in order to incorporate parameter uncertainty in our predictive inference of risk. Incorporation of our model in the overall spatio-temporal model, will contribute to the accurate and timely prediction of unusually high food-poisoning incidence, and thus to the identification and prevention of future outbreaks.